Our BPI cluster meeting on 28th March will be in Pav. K.16 between 12:30-13:30.
During that session, one PhD student Jason Rhuggenaath will talk about his current studies.
The promotor of Jason is Prof. Uzay Kaymak, and co-promoters are dr. Yingqian Zhang and dr. Alp Akcay.
Please find the title and the abstract below:
Fuzzy decision trees
A popular method in machine learning for supervised classification is decision trees. In this work we propose a new framework to learn fuzzy decision trees using mathematical programming. More specifically, we encode the problem of constructing fuzzy decision trees using a Mixed Integer Linear Programming (MIP) model, which can be solved by any optimization solver.
We compare the performance of our method with the performance of off-the-shelf decision tree algorithm CART and Fuzzy Inference Systems (FIS) using benchmark data-sets. Our initial results are promising and show the advantages of using non-crisp boundaries for improving classification accuracy on testing data.